A new approach to optimized negative selection

Early detection of threats to electrical energy distribution systems helps professionals make decisions and mitigate interruptions in supply and improper activation of the protection system. Biologically inspired methods, e.g., artificial neural networks, genetic algorithms, and ant colonies, solve optimization problems and facilitate pattern recognition and decision-making. The present work presents a tool for detecting and classifying voltage disturbances based on the negative selection algorithm, which identifies and eliminates self-reactive cells, associated with multiresolution analysis, which analyzes the signal at different scales of detail, allowing a more complete understanding and detailed description of the phenomenon in question. The negative wavelet selection algorithm demonstrates robustness to detect and classify disturbances.

A spam detection model based on negative selection algorithm is proposed in this paper. The artificial immune system creates techniques to solve complex computations, aiming to developing immune based models. This is done by distinguishing self from non-self. Preliminary mathematical analysis will expose the computation and experimental description of the method and how it is applied to spam detection. A new detector model and matching rule model are also generated for effective matching of both self and non-self in other to burst the detector performance of the model. Our unique matching technique use in the negative selection algorithm help the model to overcome the limitation of a normal negative selection algorithm in defining harmfulness of self and non-self. This improves the requirement of the model and satisfactory requirement in terms of true positive and false positive rates. The experimental result confirms that the proposed model is able to establish a better true positive on an unknown spam Models and application in artificial immune system is coming up as an active and attractive field of great diversity. Great source of inspiration to computational model are drawn from the already knowledge of the immune system. Over the past years, rapid expansion of computer network system as change the world. It is essential for an effective computer security system because attacks and criminal intend are increasingly popular in computer network(1) . There are several measure put in place by many companies in the area of creating anti spam software based on signatures and have a very efficient performance in detecting spam fast. Though, new variation of spam and unknown spam are very difficult to detect by this software. The traditional way of detecting spam based on signature is no more efficient for today systems. Recent years, researchers are interested in the field of immune system in achieving computer security. Immune theory was first applied to computer abnormality detection by Forrest et al. After then, several proposition in the area of immune system as made tremendous success. Negative selection algorithm, while not reacting to the self cells uses the immune system capability to detect unknown antigens. Its mechanism protects body against self reactive lymphocytes. Receptors are made through a pseudo-random genetic re-arrangement process during the generation of T-cells (2); they then undergo a censoring process in the thymus called the negative selection. In this process T-cells that do not bind to self-proteins are destroyed. Therefore, immunological function and protection of the body against foreign antigens is possible through circulation of matured T-cells. Artificial negative selection algorithm was proposed to follow that pattern; generating random detectors and then discard those that match self samples. It is a spam detection in which training data from only one of the classes are available. With these illustrations, the traditional negative selection algorithm believes that all self are not dangerous and all non- self

Two main techniques used to build Intrusion Detection System (IDS), abnormal detection and misuse detection, have their own strengths and limitations respectively. It implies that an effective IDS should employ an abnormal detector and a misuse detector in parallel. While the human immune system (HIS) distinguishes previously known and unknown pathogens from human body via its own passive layers which are called natural immune systems and adaptive immune systems. Natural immune system is akin to the misuse detector of IDS and adaptive immune system is similar to the abnormal detector. Inspired from adaptive immune system, Negative Selection Algorithm (NSA) is applied to abnormal detection. But NSA can not generate dynamic detectors changed with nonselves. Inspired from affinity maturation, Dynamic Negative Selection Algorithm Based on Affinity Maturation (DNSA-AM), where NSA is used to delete detectors which detect any selves, is proposed and can be adapted to the change of nonselves. Match rule with the match threshold (r) is one of the most important components in NSA and DNSA-AM. ‘r’ is related to selves and must be set at first. So DNSA-AM can not be adapted to the change of selves.

The negative selection algorithm (NSA) is one of the important methods in the field of immunological computation (or artificial immune systems). Over the years, some progress was made that turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSA’s evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA’s development and highlight challenges in NSA research in comparison with other similar models.

Negative selection algorithm is an important algorithm in the artificial immune system, inspired by the biological immune system. Traditional negative selection algorithms lack adaptive learning ability in high-dimensional space due to data sparsity and meaningless distance measurement. To solve these problems, an improved negative selection algorithm called Negative Selection Algorithm with Complete Random Subspace Technique (RS-NSA), is proposed in this paper. It adopts a bootstrap method to reduce the rate of misclassification resulting from the anomalies covered by the regions of normal samples. By using the complete random subspace technology, it reduces dimensionality to alleviate the curse of dimensionality. In addition, the ensemble learning technique is introduced to improve accuracy, in which component classifiers can be replaced by any negative selection algorithm. Empirical evaluation on UCI datasets reveals that, compared with V-detector, our proposed method can not only achieve a higher detection rate and a lower false alarm rate, but also shorten the training time.

One of the intriguing applications of immune-inspired negative selection algorithm is anomaly detection in the datasets. Such a detection is based on the self/nonself discrimination and its characteristic feature is the ability of detecting nonself samples (anomalies) by using only information about the self or regular, samples. Thus the problem space (Universe) is splitted into two disjoint subspaces: One of them contains self samples and the second is covered by the samples which activate the detectors generated by the negative selection algorithms. Hence, the efficiency of negative selection algorithms is proportional to the degree of coverage (by the detectors) of nonself subspace. In this paper, we present a simple method of increasing the coverage for real-valued negative selection algorithm.

Damage analysis in composite materials using a hybrid artificial immune system

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

Application of Negative Selection Algorithm (NSA) for test data generation of path testing

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.

This paper proposes a particle swarm optimization (PSO)-based detector optimization scheme in the negative selection algorithm (NSA). The NSA is a natural immune response inspired pattern discrimination method. In the new scheme, the NSA detectors are optimized by the PSO to collectively occupy the maximal coverage of the nonself space so that they can achieve the best anomaly detection performance. Two numerical examples including the discriminant analysis of Fisher's iris data are demonstrated to verify the effectiveness of our approach.

This paper proposes a statistical mechanism to analyze the detector coverage in a negative selection algorithm, namely a quantitative measurement of a detector set's capability to detect nonself data. This novel method has the advantage of statistical confidence in the estimation of the actual coverage. Furthermore, unlike the existing analysis works of negative selection, it doesn't depend on specific detector representation and generation algorithm. Not only can it be implemented as a procedure independent from the steps to generate detectors, the experiments in this paper showed that it can also be tightly integrated into the detector generation algorithm to control the number of detectors.

Application of the feature-detection rule to the Negative Selection Algorithm

A malware detection model based on a negative selection algorithm with a penalty factor is proposed to overcome the drawback of traditional negative selection algorithms in defining the harmfulness of self and nonself. The effectiveness of the proposed model is improved greatly by using the dangerous signatures that would have been discarded in the traditional negative selection algorithm. This chapter presents a malware detection model based on a negative selection algorithm with penalty factor (NSAPF). The proposed NSAPF model consists of a malware signature extraction module (MSEM) and a suspicious program detection module (SPDM). In the SPDM, signatures of suspicious programs are extracted using the malware instruction library (MIL). The chapter considers the malware candidate signature library (MCSL) as nonself and the BPMSL as self and generates a Malware Detection Signature Library (MDSL) using NSAPF. Comprehensive experimental results demonstrated that the proposed model is effective in detecting unknown malware with a lower FPRs.